Explainable AI-Enhanced Detection of Cyber Attacks on AGC Systems with Varying RES Penetration

This study addresses the limitations of traditional methods in detecting False Data Injection Attacks (FDIA) on Automatic Generation Control (AGC) systems, particularly due to State Estimation (SE) algorithms' tolerance for measurement errors. To overcome these limitations, six machine learning algorithms are compared across different levels of Renewable Energy Source (RES) penetration. As RES levels increase, the impact of cyber-attacks becomes more pronounced, with Convolutional Neural Networks (CNN) maintaining high performance, followed closely by the Bagged Trees (BT), algorithm as the second most successful. Additionally, Explainable Artificial Intelligence (XAI) techniques, such as Shapley Additive Explanations (SHAP), are used to clarify the role of critical features, like frequency and Area Control Error (ACE), in attack detection, making the model's decision-making process more transparent for system operators. The AGC model was implemented in MATLAB/Simulink within a two-area, four-machine power system, using realistic turbine and speed governor models rather than linearized approximations. Results show that CNN and BT are highly effective in FDIA detection within high RES environments, indicating their reliability for enhancing AGC system security under realistic operational conditions. Furthermore, the interpretability provided by SHAP assists operators in understanding how critical measurements affect detection, thus building confidence in Artificial Intelligence (AI) driven protective mechanisms for modern power grids.